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	nom	durée	vues
	L05.1 Lecture Overview	1:40	186
	L07.4 Independence of Random Variables	5:80	102
	L15.1 Lecture Overview	1:59	54
	L12.10 Interpreting the Correlation Coefficient	5:50	67
	L04.8 Each Person Gets An Ace	9:45	153
	L15.4 The Case of Multiple Observations	13:47	40
	L03.4 Independence of Event Complements	2:59	182
	L24.2 Introduction to Markov Processes	2:90	85
	S07.3 Independence of Random Variables Versus Independence of Events	6:51	89
	L05.9 Elementary Properties of Expectation	4:12	157
	L21.4 Review of Known Properties of the Bernoulli Process	2:20	57
	L25.11 Birth-Death Processes - Part II	8:57	92
	L06.5 Total Expectation Theorem	6:28	166
	L05.4 Bernoulli & Indicator Random Variables	3:60	201
	L21.8 Merging of Bernoulli Processes	7:12	43
	L21.1 Lecture Overview	2:10	63
	L09.10 Joint CDFs	4:16	72
	L12.8 The Correlation Coefficient	7:30	82
	L10.5 Independence	3:35	59
	L03.6 Independence Versus Conditional Independence	5:30	217
	L20.1 Lecture Overview	2:46	66
	L15.2 Recognizing Normal PDFs	7:15	44
	L22.3 Applications of the Poisson Process	3:30	63
	L04.3 Die Roll Example	4:39	164
	L03.1 Lecture Overview	1:26	247
	L06.6 Geometric PMF Memorylessness & Expectation	10:29	153
	L01.8 A Continuous Example	5:20	586
	L02.4 Conditional Probabilities Obey the Same Axioms	7:45	260
	L07.8 The Hat Problem	16:90	111
	L25.5 Recurrent and Transient States: Review	3:26	66
	L07.2 Conditional PMFs	10:48	144
	L11.2 The PMF of a Function of a Discrete Random Variable	6:42	78
	L10.2 Conditional PDFs	6:57	96
	L09.5 Total Probability & Expectation Theorems	6:51	91
	L16.1 Lecture Overview	1:13	57
	L22.10 An Example	14:80	30
	L14.6 Discrete Parameter, Continuous Observation	4:35	50
	L03.9 Reliability	7:28	200
	L03.7 Independence of a Collection of Events	6:00	202
	L09.7 Joint PDFs	9:18	120
	L20.5 Confidence Intervals	5:40	42
	L07.6 Independence & Expectations	4:22	98
	L12.6 Covariance Properties	5:48	79
	L11.6 The Monotonic Case	11:70	65
	L22.1 Lecture Overview	1:31	48
	L24.5 N-Step Transition Probabilities	10:59	54
	L16.5 Example: The LMS Estimate	6:31	49
	L25.6 Periodic States	6:49	40
	L01.6 More Properties of Probabilities	8:40	752
	S05.1 Supplement: Functions	8:80	134
	L07.7 Independence, Variances & the Binomial Variance	7:90	99
	L14.10 Summary	5:41	32
	L14.1 Lecture Overview	2:10	82
	L25.10 Birth-Death Processes - Part I	8:56	493
	L10.8 Bayes Rule Variations	3:27	66
	L26.1 Brief Introduction	1:41	59
	L11.1 Lecture Overview	1:52	72
	L14.5 Discrete Parameter, Discrete Observation	6:46	55
	L18.7 Convergence in Probability Examples	8:50	74
	L17.8 The Simplest LLMS Example with Multiple Observations	5:60	42
	L12.3 The Sum of Independent Continuous Random Variables	6:45	96
	L23.8 Random Incidence in a Non-Poisson Process	4:36	32
	L06.2 Variance	10:43	167
	L03.8 Independence Versus Pairwise Independence	8:35	215
	L02.3 A Die Roll Example	5:20	265
	L23.3 Merging Independent Poisson Processes	8:22	50
	L23.2 The Sum of Independent Poisson Random Variables	4:30	67
	S01.9 Proof That a Set of Real Numbers is Uncountable	4:20	422
	L12.4 The Sum of Independent Normal Random Variables	3:10	79
	L11.5 The PDF of a General Function	9:47	71
	L17.2 LLMS Formulation	4:58	35
	L05.8 Expectation	10:38	181
	L13.6 The Conditional Variance	5:20	73
	L09.6 Mixed Random Variables	5:35	95
	L25.8 A Numerical Example - Part II	3:58	41
	L08.5 Mean & Variance of the Uniform	3:56	99
	L09.9 Continuous Analogs of Various Properties	1:40	60
	L18.4 The Weak Law of Large Numbers	7:31	216
	L18.3 The Chebyshev Inequality	5:57	112
	L09.8 From The Joint to the Marginal	7:23	80
	L16.2 LMS Estimation in the Absence of Observations	6:48	51
	L20.4 On the Mean Squared Error of an Estimator	6:54	67
	L19.6 Normal Approximation to the Binomial	11:53	49
	L16.7 LMS Estimation with Multiple Observations or Unknowns	5:24	48
	L08.3 Uniform & Piecewise Constant PDFs	2:52	92
	L06.1 Lecture Overview	2:20	141
	L21.2 The Bernoulli Process	4:21	173
	L20.3 The Sample Mean and Some Terminology	4:58	114
	L26.7 Expected Time to Absorption	11:30	42
	S01.5 Infinite Series	3:11	308
	L21.7 The Time of the K-th Arrival	8:12	35
	L24.6 A Numerical Example - Part I	9:26	50
	L23.6 Splitting a Poisson Process	5:60	68
	L19.7 Polling Revisited	13:54	36
	L03.10 The King's Sibling	6:54	173
	L09.2 Conditioning A Continuous Random Variable on an Event	9:56	119
	L05.6 Binomial Random Variables	6:80	156
	L13.7 Derivation of the Law of Total Variance	4:54	100
	L10.1 Lecture Overview	1:42	79
	L08.1 Lecture Overview	1:13	98
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MIT OpenCourseWare 5 896 vidéos, +2 320 000 abonnés

MIT OpenCourseWare
5 896 vidéos, +2 320 000 abonnés